SoroushVahidi/scidocs huggingface dataset:
Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks
(2018)

This study introduces a novel conditional recycle generative adversarial network for facial attribute transformation, which can transform high-level semantic face attributes without changing the identity. In our approach, we input a source facial image to the conditional generator with target attribute condition to generate a face with the target attribute. Then we recycle the generated face back to the same conditional generator with source attribute condition. A face which should be similar to that of the source face in personal identity and facial attributes is generated. Hence, we introduce a recycle reconstruction loss to enforce the final generated facial image and the source facial image to be identical. Evaluations on the CelebA dataset demonstrate the effectiveness of our approach. Qualitative results show that our approach can learn and generate high-quality identity-preserving facial images with specified attributes.
Less is more ? How demographic sample weights can improve public opinion estimates based on Twitter data
(2016)

Twitter data is widely acknowledged to hold great promise for the study of political behavior and public opinion. However, a key limitation in previous studies is the lack of information about the sociodemographic characteristics of individual users, which raises concerns about the validity of inferences based on this source of data. This paper addresses this challenge by employing supervised machine learning methods to estimate the age, gender, race, party affiliation, propensity to vote, and income of any Twitter user in the U.S. The training dataset for these classifiers was obtained by matching a large dataset of 1 billion geolocated Twitter messages with voting registration records and estimates of home values across 15 different states, resulting in a sample of nearly 250,000 Twitter users whose sociodemographic traits are known. To illustrate the value of this approach, I offer three applications that use information about the predicted demographic composition of a random sample of 500,000 U.S. Twitter users. First, I explore how attention to politics varies across demographics groups. Then, I apply multilevel regression and postratification methods to recover valid estimate of presidential and candidate approval that can serve as early indicators of public opinion changes and thus complement traditional surveys. Finally, I demonstrate the value of Twitter data to study questions that may suffer from social desirability bias. Working paper. This version: September 21, 2016. I would like to thank John Ahlquist, Drew Dimmery, Pat Egan, Martin Elff, Mik Laver, Jeff Lewis, Thomas Leeper, Jonathan Nagler, Molly Roberts, Arthur Spirling, David Sontag for helpful comments and discussions. I also gratefully acknowledges financial support from the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation.
Effectiveness of Blog Advertising: Impact of Communicator Expertise, Advertising Intent, and Product Involvement
(2007)

Blog advertising, which refers to the paid sponsorship of bloggers to review, promote, or sell products in their blog writing, is becoming prevalent. This paper investigates the impact of three critical factors on blog advertising effectiveness: communicator expertise, advertising intent, and product involvement. An experiment with a 2×2×2 factorial design was used to test their interaction effects on advertising effectiveness. The results indicate that, for low-involvement products, there is better advertising effectiveness when low-expertise communicators are explicit about the advertising intent or when high-expertise communicators are implicit about the advertising intent. But for high-involvement products, the results show that when low-expertise communicators are explicit about the advertising intent, the outcome is lesser advertising effectiveness. For such products, advertising effectiveness does not differ when high-expertise communicators are implicit or explicit about the advertising intent. Based on these results, some implications for further research and practice are given.
Feature Analysis for Fake Review Detection through Supervised Classification
(2017)

Nowadays, review sites are more and more confronted with the spread of misinformation, i.e., opinion spam, which aims at promoting or damaging some target businesses, by misleading either human readers, or automated opinion mining and sentiment analysis systems. For this reason, in the last years, several data-driven approaches have been proposed to assess the credibility of user-generated content diffused through social media in the form of on-line reviews. Distinct approaches often consider different subsets of characteristics, i.e., features, connected to both reviews and reviewers, as well as to the network structure linking distinct entities on the review-site in exam. This article aims at providing an analysis of the main review- and reviewer-centric features that have been proposed up to now in the literature to detect fake reviews, in particular from those approaches that employ supervised machine learning techniques. These solutions provide in general better results with respect to purely unsupervised approaches, which are often based on graph-based methods that consider relational ties in review sites. Furthermore, this work proposes and evaluates some additional new features that can be suitable to classify genuine and fake reviews. For this purpose, a supervised classifier based on Random Forests have been implemented, by considering both well-known and new features, and a large-scale labeled dataset from which all these features have been extracted. The good results obtained show the effectiveness of new features to detect in particular singleton fake reviews, and in general the utility of this study.
Miniature Continuous Coverage Antenna Array for GNSS Receivers
(2008)

This letter presents a miniature conformal array that provides continuous coverage and good axial ratio from 1100-1600 MHz. Concurrently, it maintains greater than 1.5 dBic RHCP gain and return loss less than -10 dB. The four element array is comprised of two-arm slot spirals with lightweight polymer substrate dielectric loading and a novel termination resistor topology. Multiple elements provide the capability to suppress interfering signals common in GNSS applications. The array, including feeding network, is 3.5" times 3.5" times 0.8" in size and fits into the FRPA-3 footprint and radome.
A Survey on Preprocessing Methods for Web Usage Data
(2010)

World Wide Web is a huge repository of web pages and links. It provides abundance of information for the Internet users. The growth of web is tremendous as approximately one million pages are added daily. Users’ accesses are recorded in web logs. Because of the tremendous usage of web, the web log files are growing at a faster rate and the size is becoming huge. Web data mining is the application of data mining techniques in web data. Web Usage Mining applies mining techniques in log data to extract the behavior of users which is used in various applications like personalized services, adaptive web sites, customer profiling, prefetching, creating attractive web sites etc., Web usage mining consists of three phases preprocessing, pattern discovery and pattern analysis. Web log data is usually noisy and ambiguous and preprocessing is an important process before mining. For discovering patterns sessions are to be constructed efficiently. This paper reviews existing work done in the preprocessing stage. A brief overview of various data mining techniques for discovering patterns, and pattern analysis are discussed. Finally a glimpse of various applications of web usage mining is also presented. KeywordsData Cleaning, Path Completion, Session Identification , User Identification, Web Log Mining
Exploration-exploitation tradeoff using variance estimates in multi-armed bandits
(2009)

Algorithms based on upper confidence bounds for balancing exploration and exploitation are gaining popularity since they are easy to implement, efficient and effective. This paper considers a variant of the basic algorithm for the stochastic, multi-armed bandit problem that takes into account the empirical variance of the different arms. In earlier experimental works, such algorithms were found to outperform the competing algorithms. We provide the first analysis of the expected regret for such algorithms. As expected, our results show that the algorithm that uses the variance estimates has a major advantage over its alternatives that do not use such estimates provided that the variances of the payoffs of the suboptimal arms are low. We also prove that the regret concentrates only at a polynomial rate. This holds for all the upper confidence bound based algorithms and for all bandit problems except those special ones where with probability one the payoff obtained by pulling the optimal arm is IThis research was funded in part by the National Science and Engineering Research Council (NSERC), iCore and the Alberta Ingenuity Fund. ∗Corresponding author. Email addresses: audibert@certis.enpc.fr (Jean Yves Audibert), remi.munos@inria.fr (Rémi Munos), szepesva@cs.ualberta.ca (Csaba Szepesvári) 1Csaba Szepesvári is on leave from MTA SZTAKI, Budapest, Hungary. Preprint submitted to Theoretical Computer Science October 1, 2008 larger than the expected payoff for the second best arm. Hence, although upper confidence bound bandit algorithms achieve logarithmic expected regret rates, they might not be suitable for a risk-averse decision maker. We illustrate some of the results by computer simulations.
Face Verification Across Age Progression
(2005)

Human faces undergo considerable amounts of variations with aging. While face recognition systems have been proven to be sensitive to factors such as illumination and pose, their sensitivity to facial aging effects is yet to be studied. How does age progression affect the similarity between a pair of face images of an individual? What is the confidence associated with establishing the identity between a pair of age separated face images? In this paper, we develop a Bayesian age difference classifier that classifies face images of individuals based on age differences and performs face verification across age progression. Further, we study the similarity of faces across age progression. Since age separated face images invariably differ in illumination and pose, we propose preprocessing methods for minimizing such variations. Experimental results using a database comprising of pairs of face images that were retrieved from the passports of 465 individuals are presented. The verification system for faces separated by as many as nine years, attains an equal error rate of 8.5%
Design, Modeling, and Validation of a Soft Magnetic 3-D Force Sensor
(2018)

Recent advances in robotics promise a future where robots co-exist and collaborate with humans in unstructured environments, which will require frequent physical interactions where accurate tactile information will be crucial for performance and safety. This article describes the design, fabrication, modeling, and experimental validation of a soft-bodied tactile sensor that accurately measures the complete 3-D force vector for both normal and shear loading conditions. Our research considers the detection of changes in the magnetic field vector due to the motion of a miniature magnet in a soft substrate to measure normal and shear forces with high accuracy and bandwidth. The proposed sensor is a pyramid-shaped tactile unit with a tri-axis Hall element and a magnet embedded in a silicone rubber substrate. The non-linear mapping between the 3-D force vector and the Hall effect voltages is characterized by training a neural network. We validate the proposed soft force sensor over static and dynamic loading experiments and obtain a mean absolute error below 11.7 mN or 2.2% of the force range. These results were obtained for a soft force sensor prototype and loading conditions not included in the training process, indicating strong generalization of the model. To demonstrate its utility, the proposed sensor is used in a force-controlled pick-and-place experiment as a proof-of-concept case study.
Traffic engineering techniques for data center networks
(2012)

Traffic engineering consists in improving the performance of the teleco-munication networks which is evaluated by a large number of criteria. The ultimate objective is to avoid congestion in the network by keeping its links from being overloaded. In large Ethernet networks with thousands of servers, such as data centers, improving the performance of the traditional switching protocols is a crucial but very challenging task due to an exploration in the size of solution space and the complexity. Thus, exact methods are inappropriate even for reasonable size networks. Local Search (LS) is a powerful method for solving computational optimization problems such as the Vertex Cover, Traveling Salesman, or Boolean Satisfiability. The advantage of LS for these problems is its ability to find an intelligent path from a low quality solution to a high quality one in a huge search space. In this thesis, we propose different approximate methods based on Local Search for solving the class of traffic engineering problems in data center networks that implement Spanning Tree Protocol and Multiple Spanning Tree Protocol. First, we tackle the minimization of the maximal link utilization in the Ethernet networks with one spanning tree. Next, we cope with data center networks containing many spanning trees. We then deal with the minimization of service disruption and the worst-case maximal link utilization in data center networks with many spanning trees. Last, we develop a novel design of multi-objective algorithms for solving the traffic engineering problems in large data centers by taking into account three objectives to be minimized: maximal link utilization, network total load and number of used links. Our schemes reduce significantly the size of the search space by releasing the dependence of the solutions from the link cost computation iii iv in order to obtain an intended spanning tree. We propose efficient in-cremental techniques to speed up the computation of objective values. Furthermore, our approaches show good results on the credible data sets and are evaluated by the strong assessment methods. ACKNOWLEDGEMENTS First of all, I would like to thank Yves Deville and Olivier Bonaventure for the four years they spent helping me. I am very lucky to have two great advisors of two different promoting styles. Yves Deville is a very demanding supervisor who wants every smallest detail to be clear and intuitive. His ideas and his criticisms sometimes made me nervous or even angry but I could not deny the truth …
A framework for collaborative sensing and processing of mobile data streams: demo
(2016)

Emerging mobile applications involve continuous sensing and complex computations on sensed data streams. Examples include cognitive apps (e.g., speech recognition, natural language translation, as well as face, object, or gesture detection and recognition) and anticipatory apps that proactively track and provide services when needed. Unfortunately, today's mobile devices cannot keep pace with such apps, despite advances in hardware capability. Traditional approaches address this problem by computation offloading. One approach offloads by sending sensed streams to remote cloud servers via cellular networks or to cloudlets via Wi-Fi, where a clone of the app runs [2, 3, 4]. However, cloudlets may not be widely deployed and access to cloud infrastructure may yield high network delays and can be intermittent due to mobility. Morever, users might hesitate to upload private sensing data to the cloud or cloudlet. A second approach offloads to accelerators by rewriting code to use DSP or GPU within mobile devices. However, using accelerators requires substantial programming effort and produces varied benefits for diverse codes on heterogeneous devices.
Optimal Expectations and the Welfare Cost of Climate Variability
(2013)

Uncertainty about the future is an important determinant of well-being, especially in developing countries where financial markets and other market failures result in ineffective insurance mechanisms. However, separating the effects of future uncertainty from realised events, and then measuring the impact of uncertainty on utility, presents a number of empirical challenges. This paper aims to address these issues and provides supporting evidence to show that increased climate variability (a proxy for future income uncertainty) reduces farmers’ subjective well-being, consistent with the theory of optimal expectations (Brunnermeier & Parker, 2005 AER), using panel data from rural Ethiopia and a new data set containing daily atmospheric parameters. The magnitude of our result indicates that a one standard deviation (7%) increase in climate variability has an equivalent effect on life satisfaction to a two standard deviation (1-2%) decrease in consumption. This effect is one of the largest determinants of life satisfaction in rural Ethiopia. (JEL: C25, D60, I31.) ∗Alem – Department of Economics, University of Gothenburg, Sweden and Department of Agricultural and Resource Economics, UC, Berkeley, USA. E-mail: yonas.alem@economics.gu.se. Colmer – The Grantham Research Institute and Centre for Economic Performance, London School of Economics, UK. E-mail: j.m.colmer@lse.ac.uk. We are grateful to Allen Blackman, Jeffery Bookwalter, Gharad Bryan, Douglas Dalenberg, Paul Dolan, John Feddersen, Greer Gosnell, Cameron Hepburn, Derek Kellenberg, Peter Martinsson, Kyle Meng, Rob Metcalfe, Eric Neumayer, Jonathan Parker, Hendrik Wolff and seminar participants at the EfD 6th Annual Meeting, EAERE, the University of Oxford, London School of Economics, University of Montana, and Resources for the Future for helpful thoughts, comments and discussions. The first author would like to thank the Swedish International Development Agency (Sida) through the Environment for Development Initiative (EfD) at the University of Gothenburg, and the Swedish Research Council Formas through the Human Cooperation to Manage Natural Resources (COMMONS) programme for financial support. Part of this research was done while Alem was a Visiting Scholar at the Department of Agricultural and Resource Economics, University of California Berkeley. The second author would like to thank the ESRC Centre for Climate Change Economics and Policy and the Grantham Foundation for financial support. The data used in this article were collected by the University of Addis Ababa, the International Food Policy Research Institute (IFPRI), and the Centre for the Study of African Economies (CSAE). Funding for the ERHS survey was provided by the Economic and Social Research Council (ESRC), the Swedish International Development Agency (SIDA) and the United States Agency for International Development (USAID). All errors and omissions are our own.
Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes
(2015)

We introduce a novel framework for option discovery and learning transfer in complex domains that are represented as object-oriented Markov decision processes (OO-MDPs) [Diuk et al., 2008]. Our framework, Portable Option Discovery (POD), extends existing option discovery methods, and enables transfer across related but different domains by providing an unsupervised method for finding a mapping between object-oriented domains with different state spaces. The framework also includes heuristic approaches for increasing the efficiency of the mapping process. We present the results of applying POD to Pickett and Barto’s [2002] PolicyBlocks and MacGlashan’s [2013] Option-Based Policy Transfer in two application domains. We show that our approach can discover options effectively, transfer options among different domains, and improve learning performance with low computational overhead.
An efficient automated design to generate UML diagram from Natural Language Specifications
(2016)

The foremost problem that arises in the Software Development Cycle is during the Requirements specification and analysis. Errors that are encountered during the first phase of the cycle migrate to other phases too which in turn results in the most costly process than the original specified process. The reason is that the specifications of software requirements are termed in the Nature Language Format. One can easily transform the requirements specified into computer model using UML. To minimize the errors that arise in the existing system, we have proposed a new technique that enhances the generation of UML models through Natural Language requirements, which can easily provide automatic assistance to the developers. The main aim of our paper is to focus on the production of Activity Diagram and Sequence Diagram through Natural Language Specifications. Standard POS tagger and parser analyze the input i.e., requirements in English language given by the users and extract phrases, activities, etc. from the text specifies. The technique is beneficial as it reduces the gap between informal natural language and the formal modeling language. The input is the requirements laid down by the users in English language. Some stages like pre-processing, part of speech (POs), tagging, parsing, phrase identification and designing of UML diagrams occur along with the input. The application and its framework is developed in Java and it is tested on by implementing on a few technical documents.
Classification of Text Documents
(1998)

The exponential growth of the internet has led to a great deal of interest in developing useful and efficient tools and software to assist users in searching the Web. Document retrieval, categorization, routing and filtering can all be formulated as classification problems. However, the complexity of natural languages and the extremely high dimensionality of the feature space of documents have made this classification problem very difficult. We investigate four different methods for document classification: the naive Bayes classifier, the nearest neighbour classifier, decision trees and a subspace method. These were applied to seven-class Yahoo news groups (business, entertainment, health, international, politics, sports and technology) individually and in combination. We studied three classifier combination approaches: simple voting, dynamic classifier selection and adaptive classifier combination. Our experimental results indicate that the naive Bayes classifier and the subspace method outperform the other two classifiers on our data sets. Combinations of multiple classifiers did not always improve the classification accuracy compared to the best individual classifier. Among the three different combination approaches, our adaptive classifier combination method introduced here performed the best. The best classification accuracy that we are able to achieve on this seven-class problem is approximately 83 %, which is comparable to the performance of other similar studies. However, the classification problem considered here is more difficult because the pattern classes used in our experiments have a large overlap of words in their corresponding documents.